1. Technical Field
The present invention relates to vessel tree modeling.
2. Discussion of the Related Art
Segmentation and modeling of vascular structures from contrast enhanced (CE) Cardiac Computed Tomography Angiography (CTA)/Cardiac Magnetic Resonance Angiography (MRA) is often a necessary task for diagnosis, treatment planning and follow-up studies. While recent technological advances in image acquisition devices such as new multi-detector CT machines, allow an increase in the spatial resolution of image data; accurate and timely segmentation and modeling of blood vessels is still a challenging task in many applications. For example, intensity contrast may change drastically along vessels; vessels may touch each other near bright structures such as bone or other vessels; a single vessel tree can have large and small vessels due to a significant scale change; and local vessel structure may deviate from a tubular shape due to the presence of pathological conditions such as stenosis.
There are a broad variety of vessel segmentation and modeling algorithms ranging from basic thresholding and region growing to more complex deformable model techniques such as modeling vessels directly from an image, e.g., without an explicit segmentation step. Traditionally, binary vessel masks were created by a vessel segmentation algorithm as described, e.g., in A. Chung and J. A. Noble. Statistical 3D vessel segmentation using a Rician distribution. In MICCAI, pages 82-89, 1999; K. Siddiqi and A. Vasilevskiy. 3d flux maximizing flows. In International Workshop on Energy Minimizing Methods In Computer Vision, 2001; and D. Nain, A. Yezzi, and G. Turk. Vessel segmentation using a shape driven flow. In MICCAI, 2004.
Centerline models of the binary vessel masks can be extracted by shortest paths algorithms as described, e.g., in B. B. Avants and J. P. Williams. An adaptive minimal path generation technique for vessel tracking in CTA/CE-MRA volume images. In Medical Image Computing and Computer-Assisted Intervention MICCAI, pages 707-716, 2000; and T. Deschamps and L. Cohen. Fast extraction of minimal paths in 3d images and applications to virtual endoscopy. Medical Image Analysis, 5(4):281-299, 2001.
Alternatively, vessel centerlines can be constructed directly from images by the use of vesselness as described, e.g., in T. M. Koller, G. Gerig, and G. S. and D. Dettwiler. Multiscale detection of curvilinear structures in 2-d and 3-d image data. In ICCV, pages 864-869, 1995; A. F. Frangi, W. J. Niessen, K. L. Vincken, and M. A. Viergever. Multiscale vessel enhancement filtering. In MICCAI, pages 82-89, 1998; and O. Wink, W. J. Niessen, and M. A. Viergever. Multiscale vessel tracking. IEEE Trans. on Medical Imaging, 23(1):130-133, 2004, medialness filters as described, e.g., in S. Aylward, S. Pizer, E. Bullitt, and D. Eberly. Intensity ridge and widths for 3d object segmentation and description. In IEEE Proc. Workshop MMBIA, pages 131-138, 1996; and S. Aylward and E. B. E. Initialization, noise, singularities, and scale in height-ridge traversal for tubular object centerline extraction. TMI, 21(2):61-75, 2002, or superellipsoids as described, e.g., in J. A. Tyrrell, E. di Tomaso, D. Fuja, R. Tong, K. Kozak, E. B. Brown, R. Jain, and B. Roysam. Robust 3-d modeling of vasculature imagery using superellipsoids. IEEE Transactions on Medical Imaging, 2006. Surface models can then be obtained from scale information contained in these filters.
In general, vessel surface models as well as centerline models are needed for visualization and quantification of vascular structures. Traditionally, scale information stored on centerlines was used to reconstruct the surface models. However, in real applications, such constructions are often not accurate enough to quantify vascular pathologies since they are estimated from simple models due to computation reasons. Especially since small vessels such as coronaries require accurate surface reconstruction at the subvoxel level.